Iterative and adaptively segmented forward problem based non-blind deconvolution technique for analyzing SRAM margin variation effects

Abstract

This paper proposes a ringing-error-free non-blind deconvolution technique featuring an iterative and adaptively segmented forward-problem based deconvolution (IASDCN) process. Unlike the algebraic based inverse operations, this eliminates any operations of differential and division by zero to successfully circumvent the issue on an abnormal V-shaped error. This effectiveness has been demonstrated for the first time with applying to a real analysis for the effects of the Random Telegraph Noise (RTN) and/or Random Dopant Fluctuation (RDF) on the overall SRAM margin variations. It has been shown that the proposed IASDCN technique can reduce its relative errors of RTN deconvolution by 1013 to 1015 times, which are good enough for avoiding the abnormal ringing errors in the RTN deconvolution process. This enables to suppress the cdf error of the convolution of RTN with RDF (i.e., fail-bit-count error) to 1/1010 error for the conventional algorithm.